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» Evaluating learning algorithms and classifiers
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AAAI
2007
15 years 2 months ago
RETALIATE: Learning Winning Policies in First-Person Shooter Games
In this paper we present RETALIATE, an online reinforcement learning algorithm for developing winning policies in team firstperson shooter games. RETALIATE has three crucial chara...
Megan Smith, Stephen Lee-Urban, Hector Muño...
ECAI
2010
Springer
15 years 1 months ago
The Dynamics of Multi-Agent Reinforcement Learning
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
Luke Dickens, Krysia Broda, Alessandra Russo
SDM
2008
SIAM
138views Data Mining» more  SDM 2008»
15 years 1 months ago
Learning Markov Network Structure using Few Independence Tests
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Parichey Gandhi, Facundo Bromberg, Dimitris Margar...
103
Voted
ER
2004
Springer
90views Database» more  ER 2004»
15 years 5 months ago
Semantic Interpretation and Matching of Web Services
A major issue in the study of semantic Web services concerns the matching problem of Web services. Various techniques for this problem have been proposed. Typical ones include FSM ...
Chang Xu, Shing-Chi Cheung, Xiangye Xiao
150
Voted
ECAI
2004
Springer
15 years 5 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana